from math 2220 class 13pi.math.cornell.edu/~back/m222_f14/slides/sep26_v2c.pdfsep. 26, 2014 from...
TRANSCRIPT
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
From Math 2220 Class 13
Dr. Allen Back
Sep. 26, 2014
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
Your next homework on 3.2 and 3.3 will not be due soonerthan Mon 10 days from now.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
No homework quiz today.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
Prelim 1 next Tues at 7:30 pm in Malott 251 will include 3.1and the local part of 3.3 but not 3.2. (Chapter 2 as well.)We can spend as much of Monday on your review questions asyou like.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
The exam has 6 questions and is not very intricatecomputationally. Question 6 is a True/False question (not toohard) with the following directions:
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
Problem 6) (15 Points) For the questions below, please justwrite the complete word TRUE or FALSE as your answer. Forthis question ONLY, no explanations are expected.(If a statement is sometimes true but not always, your answerwould be FALSE.)
You will receive 3 points for a correct answer, 1 point for ablank and −1 point for a wrong answer.
(Thus all 5 parts blank would give you 5 out of 15 while all fivewrong will lower your total on the rest of the exam by 5.)
(The exam is 11 pages with space below the questions to writeyour answers.)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Prelim and HW
In addition to my usual Monday office hours (Evan has sometoo), I will have office hours
Fri 9/26 2:15-3:15
Tues 9/30 1-2:30
Also I’m willing to talk with anyone who likes rightafter class today though I do have an appt withEvan for 2-2:15.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Closed Set contains all its boundary points.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Bounded Set lies inside some single ball of some radius.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Compact Set both closed and bounded.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Theorem A continuous functions whose domain is a compactset always attains (i.e. “has”) an absolute maximum and anabsolute minimum.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Counterexamples when not compact:
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Procedure for finding an absolute extremum for a cont. fcn. ona compact domain:
1 Above theorem guarantees existence since the domain iscompact.
2 Look for critical points (in the interior of the domain.)
3 Investigate the boundary either using lower dimensionalcalculus or Lagrange multipliers.
4 Compare values at all candidates to find the absolute maxand min.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Find the absolute max and absolute min of
f (x , y) = xy(2− x − y)
on the square |x | ≤ 1, |y | ≤ 1.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Absolute Extrema
Find the absolute max and absolute min of
f (x , y) = xy
on (and inside) the triangle with vertices (2, 0), (10, 0), and(4, 1).
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Multivariable Taylor approximation follows (using the chainrule) easily from the 1-variable case, so we’ll start by reviewinghow the results there work out.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Taylor Series of f (x) about x = a:
Σ∞n=0
f n(a)
n!(x − a)n.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Partial sums (the k + 1’st) give the k ’th Taylor Polynomial.
Pk(x) = Σkn=0
f n(a)
n!(x − a)n.
(Pk is of degree k but has k + 1 terms because we start with0.)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
So P1(x) is the linear approximation to f (x) at x = a.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Examples:
(a)ex about x = 0 (b)ex about x = 2
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Where does the formula for Pk come from heuristically?
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Where does the formula for Pk come from heuristically?The k’th Taylor polynomial Pk(x) is the unique polynomial ofdegree k whose value, derivative, 2nd derivative, . . . k’thderivative all agree with those of f (x) at x = a.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Where does the formula for Pk come from heuristically?Or integration by parts: u = f ′(x), v ′ = 1, v = (x − b)
f (b) = f (a) +
∫ b
af ′(x) dx
f (b) = f (a) +(
(x − b)f ′(x)∣∣ba−
∫ b
a(x − b)f ′′(x) dx
)f (b) = f (a) + (b − a)f ′(a)+
∫ b
a(b − x)f ′′(x) dx
The last term is one form of the “remainder” in approximatingf (b) by the first Taylor polynomial (aka linear approximant)P1(b).
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Where does the formula for Pk come from heuristically?
Or integration by parts: u = f ′′(x), v ′ = (b − x), v = −(b−x)2
2
f (b) = f (a) + (b − a)f ′(a)+
∫ b
a(b − x)f ′′(x) dx
)f (b) = f (a) + (b − a)f ′(a)+
(−(b − x)2
2f ′′(x)
∣∣∣∣ba
−∫ b
a
−(b − x)2
2f ′′′(x) dx
)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Where does the formula for Pk come from heuristically?Or integration by parts:
f (b) = f (a) + (b − a)f ′(a)+
(−(b − x)2
2f ′′(x)
∣∣∣∣ba
−∫ b
a
−(b − x)2
2f ′′′(x) dx
)f (b) = f (a) + (b − a)f ′(a)+
(b − a)2
2f ′′(a)
+
∫ b
a
(b − x)2
2f ′′′(x) dx
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
The above “integral formula” for the remainder can be turnedinto something simpler to remember but sufficient for mostapplications:The next Taylor term except with the higher derivativeevaluated at some unknown point c between a and b.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Taylor’s Theorem with RemainderIf f (x), f ”(x), . . . f (k)(x) all exist and are continuous on [a, b]and f (k+1)(x) exists on (a, b), then there is a c ∈ (a, b) so that
f (b) = Pk(b) + Rk(b)
where Pk is the k’th Taylor polynomial of f about x = a and
Rk(b) =f k+1(c)
(k + 1)!(b − a)k+1.
Please remember this!
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Example: Accuracy of sin x byP1(x) (or P3(x)) (about x = 0)for |x | < .1? Or x < .01?
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
f (x) = 11+x+x2 about x = 1.
Approximation properties of different Taylor polynomials nearx = 1.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Taylor polynomials of f (x) = 11+x+x2 about x = 1.
Typically higher order approximate well for a greater distance.
(Bad behavior beyond the radius of convergence.)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Show that the Maclaurin series of sin x converges to sin x forall x .
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Show that the Maclaurin series of sin x converges to sin x forall x .This is different than saying the radius of convergence is ∞.Some functions, e.g.
f (x) =
{0 if x ≤ 0
e−1x if x > 0
have a Taylor series which always converges, but not to f (x)even near 0. In this case, every derivative is 0 at x = 0 and theMaclaurin series is identically 0!
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Show that the Maclaurin series of sin x converges to sin x forall x .The point here is that all derivatives are bounded functionstaking values between −1 and +1. So you can show theremainder Rn(x) goes to 0 as n→∞.Taylor polynomials of f (x) = 1
1+x+x2 about x = 1.Typically higher order approximate well for a greater distance.
(Bad behavior beyond the radius of convergence.)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
It is more advanced than our course, but there is an amazingconnection between when Taylor series converge to a functionand differentiability of f(x) for x viewed as a complex variable.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
It is more advanced than our course, but there is an amazingconnection between when Taylor series converge to a functionand differentiability of f(x) for x viewed as a complex variable.For example, consider the MacLaurin series of f (x) = 1
1+x2 .
This fcn. is complex differentiable when x 6= ±i = ±√−1. And
if one identifies the complex number a + bi with the point(a, b) ∈ R2, the nearest bad point of ±i ∼ (0,±1) is distance 1from 0 = 0 + 0i ∼ (0, 0). So from this advanced point of view,the radius of convergence is 1!
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
1 Variable Taylor Series
Similarly, consider the above picture for f (x) = 11+x+x2 about
x = 1. This fcn. is bad when 1 + x + x2 = 0, or x = −1±i√
32 .
The distance from 1 + 0i ∼ (1, 0) to −(12 ,√
32 ) is
√3, so from
this advanced point of view, that is the radius of convergencefor this Taylor series.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
Given f : U ⊂ Rn → R, to find the formula for the k’th Taylorpolynomial Pk of a C k function about the pointp0 = (a1, a2, . . . , an) ∈ U, we consider the 1-variable functiong(t) defined by
g(t) = f (p0 + th)
where h = (h1, h2, . . . , hn) ∈ Rn.If we are interested in relating the Taylor polynomial Pk to thebehavior of f at the point p = (x1, x2, . . . , xn) ∈ U we mightchoose
h = (x1 − a1, x2 − a2, . . . , xn − an)
as long as the entire line segment between p0 and p lies in U.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
g(t) = f (p0 + th)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
Problem: Find the first and second Taylor polynomials of
f (x , y) = cos (x + 3y) + sin x
about (x , y) = (π
2, π).
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
Problem: Find the first and second Taylor polynomials of
f (x , y) = cos (x + 3y) + sin x
about (x , y) = (π
2, π).
Estimate the error in approximating f by P1 for |x − π
2| < .1
and |y − π| < .1.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
Problem: Find the first and second Taylor polynomials of
f (x , y) = cos (x + 3y) + sin x
about (x , y) = (π
2, π).
Estimate the error in approximating f by P1 for |x − π
2| < .01
and |y − π| < .01.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Polynomials
Problem: Find the first and second Taylor polynomials of
f (x , y) = cos (x + 3y) + sin x
about (x , y) = (π
2, π).
Find an ε so that the error in approximating f by P1 for
|x − π
2| < ε and |y − π| < ε is at most .01.
Or at most 10−6.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
Graph of f (x , y) = 1x+y2 for .5 ≤ x , y ≤ 1.5
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
P2 about (1, 1) for f (x , y) = 1x+y2 for .5 ≤ x , y ≤ 1.5
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
Both superimposed for .5 ≤ x , y ≤ 1.5
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
Error in using P2 for f for .5 ≤ x , y ≤ 1.5
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
Error in using P2 for f for .75 ≤ x , y ≤ 1.25
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Multivariable Taylor Pictures
Error in using P2 for f for 0 ≤ x , y ≤ 3
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partial Derivatives
A typical application of mixed partials arises out of yourhomework problem 3.1 #22 where you show that the waveequation
∂2w
∂x2=∂2w
∂y2
becomes after a change of coordinates
x = u + v , y + u − v
∂2w
∂u∂v= 0
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partial Derivatives
A typical application of mixed partials arises out of yourhomework problem 3.1 #22 where you show that the waveequation
∂2w
∂x2=∂2w
∂y2
becomes after a change of coordinates
x = u + v , y + u − v
∂2w
∂u∂v= 0
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partial Derivatives
A typical application of mixed partials arises out of yourhomework problem 3.1 #22 where you show that the waveequation
∂2w
∂x2=∂2w
∂y2
x = u + v , y + u − v∂2w
∂u∂v= 0
implying the general solution
w = f (u) + g(v) = f (x − y
2) + g(
x + y
2)
for any C 2 functions f and g .
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
When f is not C 2, fxy need not equal fyx even when both exist.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This function
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This functionis clearly C 2 for (x , y) 6= (0, 0) since it is a rational function(quotient of polynomials) with nonzero denominator.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This functionsatisfies f (x , y) = −f (y , x) implying
fxy = fyx
whenever x = y . (A good chain rule exercise . . . )
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This functionsatisfies fx(0, 0) = 0 and fy (0, 0) = 0 since f vanishes along theaxes.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This function
satisfies fx =y(x2 − y2)
x2 + y2+
2x2y
x2 + y2− 2x2y(x2 − y2)
(x2 + y2)2.
implying
fx(0, y) = −y (the last two terms vanish)
and∂2f
∂y∂x(0, 0) = −1.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
A Counterexample
An example illustrating this is
f (x , y) =
{xy(x2−y2)
x2+y2 if (x , y) 6= (0, 0)
0 if (x , y) = (0, 0)
This function
fx(0, y) = −y (the last two terms vanish)
and∂2f
∂y∂x(0, 0) = −1.
By fxy (x , x) = −fyx(x , x) (mentioned above) or directly we see
∂2f
∂x∂y(0, 0) = 1
and the mixed partials really differ!
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partials in Polar Cordinates
Let x = r cos θ, y = r sin θ. Given f (x , y), express
∂2f
∂r2
in terms of partials of f with respect to x and y .
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partials in Polar Cordinates
Really there are two functions here; namely f (x , y) and
g(r , θ) = f (r cos θ, r sin θ).
But many applied fields routinely use the same letter f for bothfunctions.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partials in Polar Cordinates
The key step is realizing that the task (via the chain rule) of
relating∂f
∂rto
∂f
∂xand
∂f
∂yis just like the task of doing the
analagous thing with expressions like
∂
∂r
(∂f
∂x
).
(The right hand side “operator notation” means partially
differentiate the function∂f
∂xof (x , y) with respect to r .)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partials in Polar Cordinates
x = r cos θ y = r sin θ
∂f
∂r=
∂f
∂x
∂x
∂r+∂f
∂y
∂y
∂r
=∂f
∂xcos θ +
∂f
∂ysin θ
∂
∂r
(∂f
∂x
)=
(∂
∂x
(∂f
∂x
))∂x
∂r+
(∂
∂y
(∂f
∂x
))∂y
∂r
=
(∂2f
∂x2
)cos θ +
(∂2f
∂y∂x
)sin θ
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Higher Partials in Polar Cordinates
A similar computation with∂2f
∂θ2would show that the laplacian
∂2f
∂x2+∂2f
∂y2
is given in polar coordinates by
∂2f
∂r2+
1
r
∂f
∂r+
1
r2
(∂2f
∂θ2
).
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Can you estimate the value of the gradient?
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
Problem 33 on page 146 from your homework asks you to show
using the chain rule that if z = f (y
x) for a 1-variable
differentiable function f , then
x∂z
∂x+ y
∂z
∂y= 0.
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
Problem 33 on page 146 from your homework asks you to show
using the chain rule that if z = f (y
x) for a 1-variable
differentiable function f , then
x∂z
∂x+ y
∂z
∂y= 0.
You don’t need it to do this homework problem, but a naturalquestion is
Does x∂z
∂x+ y
∂z
∂y= 0 imply z = f (
y
x)
for some 1-variable differentiable function f ?
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
The answer is yes, and we can easily show it using what welearned in section 2.6!(i.e. essentially chain rule ideas applied to curves.)
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
The beautiful idea for the equation
a(x , y)∂z
∂x+ b(x , y)
∂z
∂y= c(x , y , z)
is to consider curves (x(t), y(t) called characteristics) satisfying
dx
dt= a(x(t), y(t))
dy
dt= b(x(t), y(t)).
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
Here for
x∂z
∂x+ y
∂z
∂y= 0
we havedx
dt= x
dy
dt= y
with solutions x(t) = x0et and y(t) = y0et .
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
Here for
x∂z
∂x+ y
∂z
∂y= 0
we havedx
dt= x
dy
dt= y
with solutions x(t) = x0et and y(t) = y0et .The key observation is that for a solution z(x , y)
d
dt[z(x(t), y(t))] =
∂z
∂x
dx
dt+
∂z
∂y
dy
dt
=∂z
∂xx+
∂z
∂yy
= 0 !
From Math2220 Class 13
V2c
Prelim andHW
AbsoluteExtrema
1 VariableTaylor Series
MultivariableTaylorPolynomials
MultivariableTaylorPictures
Higher PartialDerivatives
A Counterex-ample
Higher Partialsin PolarCordinates
GradientEstimate fromGraph
Method ofCharacteristics
Method of Characteristics
Here for
x∂z
∂x+ y
∂z
∂y= 0
with solutions x(t) = x0et and y(t) = y0et .The key observation is that for a solution z(x , y)
d
dt[z(x(t), y(t))] =
∂z
∂x
dx
dt+
∂z
∂y
dy
dt
=∂z
∂xx+
∂z
∂yy
= 0 !
So z(x , y) is constant along linesy
x= k for any k and thus
z(x , y) = z(1,y
x) = f (
y
x)
as we wanted to show.